The Forum for Information Retrieval (FIRE) started a shared task this year for classification of comments of different code segments. This is binary text classification task where the objective is to identify whether comments given for certain code segments are relevant or not. The BioNLP-IISERB group at the Indian Institute of Science Education and Research Bhopal (IISERB) participated in this task and submitted five runs for five different models. The paper presents the overview of the models and other significant findings on the training corpus. The methods involve different feature engineering schemes and text classification techniques. The performance of the classical bag of words model and transformer-based models were explored to identify significant features from the given training corpus. We have explored different classifiers viz., random forest, support vector machine and logistic regression using the bag of words model. Furthermore, the pre-trained transformer based models like BERT, RoBERT and ALBERT were also used by fine-tuning them on the given training corpus. The performance of different such models over the training corpus were reported and the best five models were implemented on the given test corpus. The empirical results show that the bag of words model outperforms the transformer based models, however, the performance of our runs are not reasonably well in both training and test corpus. This paper also addresses the limitations of the models and scope for further improvement.

翻译：信息检索论坛（FIRE）今年启动了一项针对不同代码段注释分类的共享任务。这是一个二元文本分类任务，旨在判断特定代码段的注释是否具有相关性。印度科学教育与研究学院博帕尔分校（IISERB）的BioNLP-IISERB小组参与了该任务，并针对五种不同模型提交了五次运行结果。本文概述了这些模型以及在训练语料库中发现的其他重要结论。研究方法涉及不同的特征工程方案和文本分类技术。我们探索了经典词袋模型与基于Transformer模型的性能，以从给定训练语料中识别显著特征。通过词袋模型，我们研究了随机森林、支持向量机和逻辑回归等不同分类器。此外，我们还通过微调预训练的基于Transformer的模型（如BERT、RoBERT和ALBERT）在给定训练语料上的应用。本文报告了不同模型在训练语料上的性能，并将表现最好的五种模型应用于给定测试语料。实验结果表明，词袋模型性能优于基于Transformer的模型，但我们的运行结果在训练和测试语料上的表现均不理想。本文还探讨了这些模型的局限性及未来改进方向。